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Course List

Artificial Intelligence

  • Course Code :
    CSC 341
  • Level :
    Undergraduate
  • Course Hours :
    3.00 Hours
  • Department :
    Department of Computer Science

Instructor information :

Area of Study :

Knowledge Representations: Predicate Calculus, Structured Representations, Network Representations. State Space Search: trees and graphs, heuristic search, model based reasoning, case-based reasoning, reasoning with uncertain or incomplete knowledge. Overview of AI languages, Overview of AI Application Areas.

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Artificial Intelligence

Knowledge Representations: Predicate Calculus, Structured Representations, Network Representations. State Space Search: trees and graphs, heuristic search, model based reasoning, case-based reasoning, reasoning with uncertain or incomplete knowledge. Overview of AI languages, Overview of AI Application Areas

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Artificial Intelligence

Course outcomes:

a. Knowledge and Understanding:

1- Have some understanding of the basic concepts and techniques of AI
2- Have some understanding of the basic concepts of knowledge based systems
3- Have some understanding of some blind and heuristic search techniques
4- Have some understanding of issues in knowledge acquisition, and representation
5- Have some understanding of issues in monotonic and non-monotonic Logic
6- Have some understanding of Machine Learning and Neural Networks

b. Intellectual Skills:

1- Appreciate the subtleties related to different approaches to AI
2- Appreciate the subtleties related to different AI techniques
3- Decide the suitability of AI techniques for a problem/domain
4- Analyze and design a KBS for a simple domain.

c. Professional and Practical Skills:

1- Have some practice of knowledge acquisition
2- Represent knowledge of a domain in a suitable knowledge representation formalism
3- Write simple AI programs in PROLOG or C/C++.
4- Represent and implement AI solutions to a suitable AI problems
5- Implement a KBS for a simple domain

d. General and Transferable Skills:

1- Deploy communication skills
2- Deploy research skills
3- Work effectively within a group to analyze, design and implement an Intelligent Systems
4- To work to tight deadlines
5- Effectively present the final work in a demo
6- Justify students design decisions in a written document

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Artificial Intelligence

Course topics and contents:

Topic No. of hours Lecture Tutorial/Practical
Knowledge Representation (Semantic Nets. –Frame) 4 2 2
2nd Mid-Term Exam 4 2 2
Geometric analogy net 4 2 2
Recording Cases 4 2 2
AI Topics 4 2 2
Revision 4 2 2
Final Exam 4 2 2
Introduction to AI Concepts 4 2 2
Problems and Problem space 4 2 2
Problem Characteristics 4 2 2
AI-Search 4 2 2
1st Mid-Term Exam 4 2 2
Knowledge Acquisition 4 2 2
Knowledge Representation (Production Rules) 4 2 2

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Artificial Intelligence

Teaching And Learning Methodologies:

Teaching and learning methods
Lectures
Practical training
Projects
Web-Site searches

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Artificial Intelligence

Course Assessment :

Methods of assessment Relative weight % Week No. Assess What

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Artificial Intelligence

Books:

Book Author Publisher
Artificial Intelligence Amodern Approach Stuart Russell Pearson

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